A neural stochastic multiscale optimization framework for sensor-based parameter estimation

Article type: Research Article

Authors: Banchs, Rafael E.^{; *} | Klie, Hector | Rodriguez, Adolfo | Thomas, Sunil G. | Wheeler, Mary F.

Affiliations: CSM, ICES, The University of Texas at Austin, Texas, USA. E-mail: [email protected], [email protected], [email protected], [email protected]

Correspondence: [*] Corresponding author: Department of Signal Theory and Communications, Polytechnic University of Catalonia, Barcelona, Spain. E-mail: [email protected].

Abstract: This work presents a novel neural stochastic optimization framework for reservoir parameter estimation that combines two independent sources of spatial and temporal data: oil production data and dynamic sensor data of flow pressures and concentrations. A parameter estimation procedure is realized by minimizing a multi-objective mismatch function between observed and predicted data. In order to be able to efficiently perform large-scale parameter estimations, the parameter space is decomposed in different resolution levels by means of the singular value decomposition (SVD) and a wavelet upscaling process. The estimation is carried out incrementally from low to higher resolution levels by means of a neural stochastic multilevel optimization approach. At a given resolution level, the parameter space is globally explored and sampled by the simultaneous perturbation stochastic approximation (SPSA) algorithm. The sampling yielded by SPSA serves as training points for an artificial neural network that allows for evaluating the sensitivity of different multi-objective function components with respect to the model parameters. The proposed approach may be suitable for different engineering and scientific applications wherever the parameter space results from discretizing a set of partial differential equations on a given spatial domain.

DOI: 10.3233/ICA-2007-14302

Journal: Integrated Computer-Aided Engineering, vol. 14, no. 3, pp. 213-223, 2007